Repeated biopsies in patients with prostate cancer on active surveillance: clinical implications of interobserver variation in histopathological assessment
Bibliographic record
Abstract
OBJECTIVE: To investigate the clinical implications of interobserver variation in the assessment of re-biopsies obtained during active surveillance (AS) of prostate cancer. PATIENTS AND METHODS: In all, 107 patients with low-risk prostate cancer with 93 diagnostic biopsy sets and 109 re-biopsy sets were included. The International Society of Urological Pathology 2005 Gleason scoring system was used for the histopathological assessment of all biopsies. Three different definitions of histopathological progression were applied. Unweighted and linear weighted Kappa (κ) statistics were used to compare the interobserver agreement. RESULTS: The overall Gleason score agreement was 68.8% with a weighted κ of 0.670. The interobserver agreement was 79.6% for meeting the AS selection criteria. According to the three progression definitions applied, overall agreement was between 80.7% and 89.0% with weighted κ values of 0.746-0.791. Treatment recommendations would have changed in up to 10.1% (95% confidence interval 5.4-17.7%) of the 109 re-biopsy sets. CONCLUSION: Kappa statistics showed strong agreement between the histological evaluations. However, up to 10% of patients on AS would receive a different treatment recommendation depending upon which histopathological evaluation of re-biopsies was used for treatment planning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".